SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 1589 papers

TitleStatusHype
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization—0
Super-resolution Reconstruction of Single Image for Latent features—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation—0
Super-Resolution with Deep Adaptive Image Resampling—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net—0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
Supervised Deep Kriging for Single-Image Super-Resolution—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge—0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture—0
Task Decoupled Framework for Reference-Based Super-Resolution—0
Task-driven real-world super-resolution of document scans—0
Task-driven single-image super-resolution reconstruction of document scans—0
Task-Driven Super Resolution: Object Detection in Low-resolution Images—0
TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified